Graph structure learning. Many STGNNs [1]}, [2]}, [3]} depend on a pre-defined graph to indicate the relationship between nodes (i.e., time series).
However, such a graph is not available or is incomplete in many cases.
An intuitive idea is to train a matrix \(\mathbf {A}\in \mathbb {R}^{N\times N}\) , where \(\mathbf {A}_{ij}\in [0,1]\)  indicates the dependency between time series \(i\)  and \(j\) .
However, since the learning of graph structure and STGNNs are coupled compactly, and there is no supervised loss information for graph structure learning [4]}, optimizing such a contiguous matrix usually leads to a complex bilevel optimization problem [5]}.
In addition, the dependency \(\mathbf {A}_{ij}\)  is usually measured by the similarity between time series, which is also a challenging task.
